Intern AI Engineer R&D

Smith & Nephew•Andover, MA
•Hybrid

About The Position

Smith+Nephew’s Early Innovation team is offering a 12-week R&D internship focused on AI engineering for future sports medicine technologies. Based in Andover, Massachusetts, with some remote flexibility, the intern will help develop and evaluate AI, machine learning, and computer vision solutions. The work contributes to the TESSA software ecosystem and early-stage R&D concepts, in collaboration with software, systems, algorithm, and cross-functional engineering teams. A structured onboarding program is provided. The planned start date is May 2027. The posting notes that the opportunity is not currently available for hiring and that the requisition was established to collect expressions of interest until formal hiring can proceed. Visa transfer or sponsorship is not offered now or in the future.

Requirements

  • Be a current college junior or senior in computer science, computer engineering, electrical engineering, biomedical engineering, data science, AI/ML, robotics, or a related engineering field.
  • Have coursework, projects, or experience using Python.
  • Demonstrate curiosity, strong problem-solving and communication skills, and the ability to work on a cross-functional team.

Nice To Haves

  • A GPA of 3.3 or higher; strong academic standing is preferred.
  • Previous internship, co-op, research, project, or regulated-industry experience.
  • Familiarity with PyTorch, TensorFlow, or OpenCV.
  • Exposure to Git, software testing, debugging, technical documentation, or statistical methods.

Responsibilities

  • Develop and test AI, machine learning, and computer vision algorithms for medical imaging, navigation, visualization, and data analysis.
  • Prototype and integrate software within the TESSA ecosystem.
  • Explore AI-enabled workflows, automation, analytics, and proof-of-concept solutions.
  • Analyze data, define performance metrics, and document methods and results.
  • Share findings, demonstrations, and recommendations with engineering teams and R&D leaders.

Benefits

  • Hands-on, team-customized training and mentorship.
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